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Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast’s Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests.

Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about folia...

This study investigates the potential of different vegetation descriptors (V) in the modified water cloud model (MWCM), with a focus on comparing the Red – Edge vegetation indices (VI) based and ot...

In this study, a comparative analysis of three satellite precipitation products including the Tropical Rainfall Measuring Mission (TRMM-3B43 V7), the Precipitation Estimation from Remotely Sensed I...

In the face of rapid urbanization, monitoring urban expansion has gained importance to sustainably manage the land resources and minimize the impact on the environment. Monitoring urban growth using satellite data involves computing the state of land use–land cover and their change over time. A number of computing methods have been developed to process and interpret the satellite results for an urban environment. However, due to a large number of parametric and nonparametric algorithms used for land-use–land-cover classification, there is uncertainty regarding choosing the best algorithm to measure the urban processes. In this study, several parametric (maximum likelihood) and nonparametric (support vector machine, spectral angle mapper, artificial neural network and decision tree) algorithms were used. The study was aimed at finding out the best available classification technique for land-use–land-cover classification and change detection. Landsat 8, the latest in Landsat series, and Landsat 7 and 5 freely available satellite data were used. Due to the redundancy reported for the traditional kappa-based indices, we applied modern disagreement indices to assess the accuracy of the classification process. Artificial neural network for Landsat 8 image had the highest kappa coefficient, while spectral angle mapper had the highest overall agreement (97%) and least quantity allocation error (1%). Spectral Angle Mapper gave the highest accuracy, while maximum likelihood classification gave the least for allocation and spatial disagreement indices. We found that spectral angle mapper gave the best results for land-use–land-cover change analysis in terms of least omission and commission errors (2.5% each) and highest overall agreement, whereas artificial neural network performed better in land-use–land-cover classification studies.

Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the original TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is required to avoid the calibration effects of datasets.

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydro-meteorological datasets. This study addresses the performance analysis of Reference Evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state of the art Hamon’s and Penman-Monteith methods were utilized for the ETo estimation in the Northern India. The performances indices such as Bias, Root Mean Square Error (RMSE) and correlation(r) were calculated, which showed the values 0.242, 0.422 and 0.959 for NCEP data (without downscaling) and 0.230, 0.402,0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with Bias, RMSE and correlation values of 0.154 0.348 and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less complicated than those required with NASA/POWER and WRF.

Flash flood is an uncertain and most catastrophic disaster worldwide that causes socio-economic problems, devastation and loss of infrastructure. One of the major triggering factors of flash floods is the extreme events like cloudburst that causes flooding of area within a short span of time. Therefore, this study aims to understand the variations in hydro-meteorological variables during the devastating Kedarnath cloudburst in the Uttarakhand, India. The hydro-meteorological variables were collected from the global satellites such as Moderate Resolution Imaging Spectroradiometer, Tropical Rainfall Measuring Mission, modelled datasets from Decision Support System for Agrotechnology Transfer and National Center for Environmental Prediction (NCEP). For the validation of satellite meteorological data, the NCEP Global analysis data were downscaled using Weather Research and Forecasting model over the study area to achieve the meteorological variables’ information. The meteorological factors such as atmospheric pressure, atmospheric temperature, rainfall, cloud water content, cloud fraction, cloud particle radius, cloud mixing ratio, total cloud cover, wind speed, wind direction and relative humidity were studied during the cloudburst, before as well as after the event. The outcomes of this study indicate that the variability in hydro-meteorological variables over the Kedarnath had played a significant role in triggering the cloudburst in the area. The results showed that during the cloudburst, the relative humidity was at the maximum level, the temperature was very low, the wind speed was slow and the total cloud cover was found at the maximum level. It is expected that because of this situation a high amount of clouds may get condensed at a very rapid rate and resulted in a cloudburst over the Kedarnath region.

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.

Species diversity quantification is a crucial step towards the biodiversity conservation and ecosystem health. The technological advancements and existing limitations of multispectral remote sensing has increased the popularity of hyperspectral remote sensing which found its use in the estimation of species diversity. The contiguous narrow bands available in hyperspectral data enables the improvised assessment of diversity index but the overlapping of the information could result in the redundancy that needs to be handled. Due to this, the idenfication of optimal bands is very important; hence, the current study provides modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS), India. Narrow hyperspectral bands of EO-1 Hyperion image were screened and the best optimum wavelength from visible and Near Infrared (NIR) regions were identified based on coefficient of determination (r2) between band reflectance and in situ measured species diversity. For in situ species diversity measurements, quadrat sampling was carried out in SWS and different Diversity Indices (DIs) namely the Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI were calculated. The identified optimum wavelengths were then employed for modifying 38 existing spectral indices which were then investigated for testing their relation with the in situ DIs. The obtained optimum bands in visible and NIR regions were found to be in correspondence with four DIs. Among several indices used in this study, during validation, modified Non-linear index, modified Red Edge Position Index, modified Structure Insensitive Pigment Index and modified Red Green Ratio Index were identified as the best hyperspectral indices for determining Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI, respectively.

Rainfall and land use/land cover changes are significant factors that impact the soil erosion processes. Therefore, the present study aims to investigate the impact of rainfall and land use/land cover changes in the current and future scenarios to deduce the soil erosion losses using the state-of-the-art Revised Universal Soil Loss Equation (RUSLE). In this study, we evaluated the long-term changes (period 1981–2040) in the land use/land cover and rainfall through the statistical measures and used subsequently in the soil erosion loss prediction. The future land use/land cover changes are produced using the Cellular Automata Markov Chain model (CA-Markov) simulation using multi-temporal Landsat datasets, while long term rainfall data was obtained from the Coupled Model Intercomparison Project v5 (CMIP5) and Indian Meteorological Department. In total seven CMIP5 model projections viz Ensemble mean, MRI-CGCM3, INMCM4, canESM2, MPI-ESM-LR, GFDL-ESM2M and GFDL-CM3 of rainfall were used. The future projections (2011–2040) of soil erosion losses were then made after calibrating the soil erosion model on the historic datasets. The applicability of the proposed method has been tested over the Mahi River Basin (MRB), a region of key environmental significance in India. The finding showed that the rainfall-runoff erosivity gradually decreases from 475.18 MJ mm/h/y (1981–1990) to 425.72 MJ mm/h/y (1991–2000). A value of 428.53 MJ mm/h/y was obtained in 2001–2010, while a significantly high values 661.47 MJ mm/h/y has been reported for the 2011–2040 in the ensemble model mean output of CMIP5. The combined results of rainfall and land use/land cover changes reveal that the soil erosion loss occurred during 1981–1990 was 55.23 t/ha/y (1981–1990), which is gradually increased to 56.78 t/ha/y in 1991–2000 and 57.35 t/ha/y in 2000–2010. The projected results showed that it would increase to 71.46 t/h/y in 2011–2040. The outcome of this study can be used to provide reasonable assistance in identifying suitable conservation practices in the MRB.

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections. The authors acknowledge financial support from the Spanish Government, Grant RTI2018-354 094336-B-I00 (MCIU/AEI/FEDER, UE), the Spanish Carlos III Health Institute, COV 20/01213, and the Basque Government, Grant IT1207-19.

Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post- fire conditions. The GIS-based modeling was based on a Multi Criterion Evaluation (MCE) technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.

Soil loss through erosion and its subsequent deposition is considered as an important challenge for watersheds. In this paper, attempt has been made to integrate the Revised Universal Soil Loss Equ...

Rapid growing urban population has resulted in the occupancy of large proportionate of the city and its outskirts, thereby contributing factors to change in the environmental conditions. This has resulted in widespread land acquisition for built up and industrial development, covering the centre of the city while moving at the outskirts of the city as well. Land Use /Land Cover (LULC) changes causes alterations in the land use categories, mostly the concrete forests which has increased the urban temperature as compared to the rural regions due to rapidly growing urbanized environment. Urban Heat Island (UHI) is one of the human-induced environmental phenomenon affecting the urban inhabitant in many ways, such as altering and disturbing the land cover its use which changes thermal energy flow causing elevated surface and air temperature. Temporal satellite datasets (LANDSAT ETM+ image of 1989, 2000 and 2006) can be used to monitor surface temperature while vegetation indices can be used to assess the coverage of the vegetation and non-vegetation area in the region. Temporal NDVI is employed in the study area to analyse the impact of land surface temperature against NDVI in the region. Therefore, temporal remotely sensed data can be used to map LULC and its dynamic changes and other environmental phenomena such as surface temperature over a period of time. Temporal UHI has been estimated using geospatial technology to incorporate it for environmental impact assessment on the surrounding environment. The present research focuses on temporal NDVI and Surface temperature, the methodology used altogether for the assessment of resolution dynamic UHI change on environmental condition for Haridwar district, Uttrakhand India and Kanpur district, Uttar Pradesh in India. Both case study has different environmental conditions, geographical locations and demography. Hilly and forested region with almost no industrial activities for Haridwar while several industrial activities and densely populated region Kanpur located in an Indo-Gangetic plain. The research outcome demonstrates the correlation between temporal NDVI and Surface temperature exemplified with case study conducted over two different regions, geographically as well as economically. There is a need to consider the environmental dimension while making progress to urbanization.

Change in soil moisture regime is highly relevant for agricultural drought, which can be best analyzed in terms of Soil Moisture Deficit Index (SMDI). A macroscale hydrological model Variable Infiltration Capacity (VIC) was used to simulate the hydro-climatological fluxes including evapotranspiration, runoff, and soil moisture storage to reconstruct the severity and duration of agricultural drought over semi-arid region of India. The simulations in VIC were performed at 0.25° spatial resolution by using a set of meteorological forcing data, soil parameters and Land Use Land Cover (LULC) and vegetation parameters. For calibration and validation, soil parameters obtained from National Bureau of Soil Survey and Land Use Planning (NBSSLUP) and ESA's Climate Change Initiative soil moisture (CCI-SM) data respectively. The analysis of results demonstrates that most of the study regions (> 80 %) especially for central northern part are affected by drought condition. The year 2001, 2002, 2007, 2008 and 2009 was highly affected by agricultural drought. Due to high average and maximum temperature, we observed higher soil evaporation that reduces the surface soil moisture significantly as well as the high topographic variations; coarse soil texture and moderate to high wind speed enhanced the drying upper soil moisture layer that incorporate higher negative SMDI over the study area. These findings can also facilitate the archetype in terms of daily time step data, lengths of the simulation period, various hydro-climatological outputs and use of reasonable hydrological model.

Climate model faces considerable difficulties in simulating the rainfall characteristics of southwest summer monsoon. In this study, the dynamical downscaling of European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA-Interim (EIN15) has been utilized for the simulation of Indian summer monsoon (ISM) through the Regional Climate Model version 4.3 (RegCM-4.3) over the South Asia Co-Ordinated Regional Climate Downscaling EXperiment (CORDEX) domain. The complexities of model simulation over a particular terrain are generally influenced by factors such as complex topography, coastal boundary, and lack of unbiased initial and lateral boundary conditions. In order to overcome some of these limitations, the RegCM-4.3 is employed for simulating the rainfall characteristics over the complex topographical conditions. For reliable rainfall simulation, implementations of numerous lower boundary conditions are forced in the RegCM-4.3 with specific horizontal grid resolution of 50 km over South Asia CORDEX domain. The analysis is considered for 30 years of climatological simulation of rainfall, outgoing longwave radiation (OLR), mean sea level pressure (MSLP), and wind with different vertical levels over the specified region. The dependency of model simulation with the forcing of EIN15 initial and lateral boundary conditions is used to understand the impact of simulated rainfall characteristics during different phases of summer monsoon. The results obtained from this study are used to evaluate the activity of initial conditions of zonal wind circulation speed, which causes an increase in the uncertainty of regional model output over the region under investigation. Further, the results showed that the EIN15 zonal wind circulation lacks sufficient speed over the specified region in a particular time, which was carried forward by the RegCM output and leads to a disrupted regional simulation in the climate model.

This study aimed at evaluating the three advanced satellite precipitation products (SPPs), i.e. CMORPH, TRMM 3B42V7 and PERSIANN, against the ground observation to evaluate their performance in detecting rain, capturing storms and rainfall pattern during 2014–2015 extreme flood events at three different river basins in Peninsular Malaysia (Kelantan, Langat and Johor river basins). Several spatial interpolation methods, including Arithmetic Mean, Thiessen Polygon, Inverse Distance Weighting, Ordinary Kriging and Spline were applied on the ground observations to transform the point-based precipitation into areal precipitation. Slight variations in the interpolated values were found, but overall it was comparable. Based on the daily rainfall data for the duration of 62 days, this study found that all SPPs performed with acceptable accuracy, as shown by the Kelantan river basin; however, these SPPs did not estimate accurately for Langat and Johor river basins. Overall, TRMM and CMORPH outperformed PERSIANN for the Langat and Johor river basins. In conclusion, all SPPs were capable of predicting heavy rainfall during the northeast monsoon and the level of accuracy is promising for the northern part of Peninsular Malaysia. However, as for the rest of the region, careful consideration should be given when applying the SPPs.

Soil moisture (SM) plays an important role in the water and energy exchanges that occur in the terrestrial surface. Soil moisture can be retrieved at a larger scale by using the visible and InfraRed (IR) bands as well as through the microwave remote sensing. Because of very high importance of SM for variety of applications, there are now two dedicated microwave satellites in the Earth’s orbit for soil moisture retrieval from space. The first, Soil Moisture and Ocean Salinity (SMOS) satellite has been launched by the European Space Agency in November 2009 and second is Soil Moisture Active and Passive (SMAP) launched by the National Aeronautics and Space Administration (NASA) in January 2015. In this review, brief background of soil moisture retrieval algorithms are presented with different applications in the area of water resources. The first section provides the introduction of the soil moisture, presents several in situ techniques for measurement of soil moisture and soil moisture retrieval algorithms from visible/IR and microwave remote sensing. Section 2 describes the satellite soil moisture applications in water resources.

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazard...

The vegetation water content (VWC) of kidney bean crop is retrieved using ground-based multi-temporal, multi-angular and co-polarized scatterometer data at X-band. An outdoor crop-bed was prepared to observe the scatterometer response at HH- and VV-polarizations in the angular range from 20° to 70°. The trend of scattering coefficient is found to decrease for the entire angular range. The present study is carried out to investigate the retrieval for VWC of a kidney bean crop at its nine different growth stages by an empirical relation based on least square optimization method using scatterometer data. The results are found promising for the retrieval of the VWC of kidney bean crop at its several growth stages.

This study aims to analyse the processes and patterns of peri-urbanization using diurnal earth observation data-sets from onboard DMSP–Operational Linescan System. In this study, multiple correlation, simple and conditional linear regression are used to find out the degree of relationship and spatial behavioural pattern of the factors responsible for the urbanization. All the factors are standardized using the Analytical Hierarchy Process (AHP) coupled fuzzy membership functions. AHP is used to derive the weighting of the factors to produce the urbanity index. In total three functional zones – urban, rural and urban shadow are generated based on factor standardization and spatial contiguity index. Urban fringe is sharing ≥ 60% of Urbanity Index followed by rural fringe (39.50–60% of urbanity index) and urban shadow

Floods are among the most destructive natural disasters causing huge loss to life and property. Any flood management strategy requires floodplain mapping through discrimination of the flood prone areas. The city of Varanasi or Benaras is believed to be the oldest continuously inhabited city of the world. This study aims to develop tools for mapping and discrimination of floodplain of river Ganga at Varanasi. During 2014 floods, the flooded areas were extracted through Normalized Difference Water Index (NDWI) and by Modified NDWI (MNDWI) using the NIR and SWIR bands separately from that of the Landsat 8 satellite imagery. The inundated areas were then identified through Support Vector Machines (SVMs) classification. The results reveal that the MNDWI images provide a better result for flood discrimination than the NDWI images. Ground based measurements for floodplain distance varied between 11 ± 5 m at Janki ghat (bank) and 80 ± 5 m at Asi ghat. The validation between measured and SVMs derived values indicate a strong positive correlation of 0.88 and a low value of Root Mean Square Error (RMSE) of 12.62. The t-test is suggestive of no significant difference between the observed and SVMs values at 95% confidence level, indicating a satisfactory performance of the SVMs for floodplain mapping using Landsat 8 imagery. Therefore, the methodology proposed in this study provides a novel and robust way for floodplain mapping and has potential applications in disaster management and mitigation in the flood affected regions.

The work assessed effect of diurnal tidal fluctuation on transient groundwater dynamics with special focus on trace metal mobilization using tide-aquifer interaction technique in the Saijo plain, Japan. Fluctuation of trace metal concentration in groundwater during intertidal phase obtained through geochemical analysis is validated with numerical simulation using two different codes (PHREEQC and SEEP/W) to observe saturation index for different minerals in aquifers and dynamics of submarine groundwater discharge (SGD) respectively. Result for saturation index shows that most of the samples are strongly undersaturated with respect to FeS, goethite, siderite and scorodite, unlike pyrite where it approaches towards saturation. Also during lower low tide situation, water samples relatively getting more undersaturated with pyrite suggesting it as a source for dissolved iron. For numerical simulation, problem domain consists of 368 elements and 3 layers, is an anisotropic unconfined aquifer with horizontal hydraulic conductivities (K X ) ranging from 0.00001 to 0.01 m/P. Coastal side of the domain considered as variable head boundary keeping different diurnal tidal amplitude into account. Simulation result shows that during low tide situation, value of SGD is maximum i.e. 2.1644e−005 and 3.3704e−005 m3/s for lower boundary towards sea at 2 and 4 m below mean sea level respectively. It suggests that tidal height affects the amount and position of SGD to play a positive role for trace metal mobilization through oxidation–reduction process. Strong relation between diurnal fluctuations of the simulated results (SGD) versus observed result oxidation–reduction potential firmly supports and validates the results from chemical analysis.

Forests are the potential source for managing carbon sequestration, regulating climate variations and balancing universal carbon equilibrium between sources and sinks. Further, assessment of biomass, carbon stock, and its spatial distribution is prerequisite for monitoring the health of forest ecosystem. Moreover, vegetation field inventories are valuable source of data for estimating aboveground biomass (AGB), density, and the carbon stored in biomass of forest vegetation. In view of the importance of biomass, the present study makes an attempt to estimate temporal AGB of Tripura State, India, using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), leaf area index (LAI) and the field inventory data through geospatial techniques. A model was developed for establishing the relationship between biomass, LAI, and NDVI in the selected study site. The study also aimed to improve method for quantifying and verifying inventory-based biomass stock estimation. The results demonstrate the correlation value obtained between LAI and NDVI were 0.87 and 0.53 for the years 2011 and 2014, respectively. The correlation value between estimated AGB with LAI were found as 0.66 and 0.69, while with NDVI, the values were obtained as 0.64 and 0.94 for the years 2011 and 2014, respectively. The regression model of measured biomass with MODIS NDVI and LAI was developed for the data obtained during the period 2011–2014. The developed model was used to estimate the spatial distribution of biomass and its relationship between LAI and NDVI. The R2 values obtained were 0.832 for estimated and the measured AGB during the training and 0.826 for the validation. The results indicate that the methodology adopted in this study can help in selecting best fit model for analyzing relationship between AGB and NDVI/LAI and for estimating biomass using allometric equation at various spatial scales. The developed output thematic map showed an average biomass distribution of 32–94 Mg ha−1. The highest biomass values (72–95 Mg ha −1) was confined to the dense region of the forest while the lowest biomass values (32–46 Mg ha−1) was identified in the outer regions of the study site.

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec&reg, 3, where the spectrum varies between 350 and 2500 nm, (ii) build field- and lab-based spectral libraries, (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer, (iv) identify the specific spectral regions associated to the modelling of HMSC, and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.

When on March 24, 2020 the Government of India ordered a complete lockdown of the country as a response to the COVID-19 pandemic, it had serious unwanted implications for farmers and the supply chains for agricultural produce. This was magnified by the fact that, as typically in developing countries, India's economy is strongly based on farming, industrialization of its agricultural systems being only modest. This paper reports on the various consequences of the COVID-19 lockdown for farming systems in India, including the economy, taking into account the associated emergency responses of state and national governments. Combining quantitative and qualitative sources of information with a focus on the Indian state of Uttar Pradesh, including expert elicitation and a survey of farmers, the paper identifies and analyzes the different factors that contributed to the severe disruption of farming systems and the agricultural sector as a whole following the lockdown. Among other issues, our study finds that the lack of migrant labor in some regions and a surplus of workers in others greatly affected the April harvest, leading to a decline in agricultural wages in some communities and an increase in others, as well as to critical losses of produce. Moreover, the partial closure of rural markets and procurement options, combined with the insufficient supply of products, led to shortages of food supplies and dramatically increased prices, which particularly affected urban dwellers and the poor. We argue that the lessons learned from the COVID-19 crisis could fuel the development of new sustainable agro-policies and decision-making in response not only to future pandemics but also to the sustainable development of agricultural systems in India and in developing countries in general.

Ozone dynamics in two urban background atmospheres over middle Indo-Gangetic Plain (IGP) were studied in two contexts: total columnar and ground-level ozone. In terms of total columnar ozone (TCO), emphases were made to compare satellite-based retrieval with ground-based observation and existing trend in decadal and seasonal variation was also identified. Both satellite-retrieved (Aura Ozone Monitoring Instrument-Differential Optical Absorption Spectroscopy (OMI-DOAS)) and ground-based observations (IMD-O3) revealed satisfying agreement with OMI-DOAS observation over predicting TCO with a positive bias of 7.24 % under all-sky conditions. Minor variation between daily daytime (r = 0.54; R 2 = 29 %; n = 275) and satellite overpass time-averaged TCO (r = 0.58; R 2 = 34 %; n = 208) was also recognized. A consistent and clear seasonal trend in columnar ozone (2005-2015) was noted with summertime (March-June) maxima (Varanasi, 290.9 ± 8.8; Lucknow, 295.6 ± 9.5 DU) and wintertime (December-February) minima (Varanasi, 257.4 ± 10.1; Lucknow, 258.8 ± 8.8 DU). Seasonal trend decomposition based on locally weighted regression smoothing technique identified marginally decreasing trend (Varanasi, 0.0084; Lucknow, 0.0096 DU year-1) especially due to reduction in monsoon time minima and summertime maxima. In continuation to TCO, variation in ground-level ozone in terms of seasonality and precursor gases were also analysed from September 2014 to August 2015. Both stations registered similar pattern of variation with Lucknow representing slightly higher annual mean (44.3 ± 30.6; range, 1.5-309.1 μg/m3) over Varanasi (38.5 ± 17.7; range, 4.9-104.2 μg/m3). Variation in ground-level ozone was further explained in terms water vapour, atmospheric boundary layer height and solar radiation. Ambient water vapour content was found to associate negatively (r = -0.28, n = 284) with ground-level ozone with considerable seasonal variation in Varanasi. Implication of solar radiation on formation of ground-level ozone was overall positive (Varanasi, 0.60; Lucknow, 0.26), while season-specific association was recorded in case of atmospheric boundary layer.

Morphometric analysis is a promising technique for watershed management. It provides quantitative descriptions of river basin and useful for understanding the behaviour of basin. This study is conducted in Pahuj river basin (Bundelkhand Region) Jhansi, Central India to understand the basin characteristics for watershed prioritization. The Shuttle Radar Topography Mission satellite (SRTM) is used to derive the Digital Elevation Model (DEM) and for creation of thematic layers such as drainage order, drainage density and slope map. In total, 20 mini-watersheds are generated for understanding the morphometric parameters and estimating the compound factor for mini-watersheds. For watershed prioritization, soil hydraulic parameter, compound factor and monthly average monsoon precipitation from TRMM (Tropical Rainfall Measure Mission) for 18 years period (1998–2015) are used. The overall analysis indicates that the mini-watershed numbers 18, 19 needs utmost attention for water conservation followed by mini-watershed number 20. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which morphometric analysis are needed.

This study aimed at evaluating the accuracy of the evapotranspiration (ET) operational estimates from the Meteosat Second Generation (MSG) Spinning Enhanced Visible Infra-Red Imager (SEVIRI) at a range of selected ecosystems in Europe. For this purpose in-situ eddy covariance measurements were used, acquired from 7 selected experimental sites belonging to the CarboEurope ground observational network over 2 full years of observations (2010–2011). Appraisal of ET accuracy was also investigated with respect to land cover, season and each site(s) degree of heterogeneity, the latter being expressed by the fractional vegetation cover (FVC) operational product of SEVIRI. Results indicated a close agreement between the operational product’s ET estimates and the tower based in-situ ET measurements for all days of comparison, showing a satisfactory correlation (r of 0.709) with accuracies often comparable to previous analogous studies. For all land cover types, the grassland and cropland sites exhibited the closest agreement (r from 0.705 to 0.759). In terms of seasons the strongest correlations were observed during the summer and autumn (r of 0.714 & 0.685 respectively), and with FVC the highest correlation of 0.735 was observed for the class FVC 0.75-1 when compared against the observed values for the complete monitoring period. Our findings support the potential value of the SEVIRI ET product for regional to mesoscale studies and corroborate its credibility for usage in many practical applications. The latter is of particular importance for water limiting environments, such as those found in the Mediterranean basin, as accurate information on ET rates can provide tremendous support in sustainable water resource management as well as policy and decision making in those areas.

Crop classification is needed to understand the physiological and climatic requirement of different crops. Kernel-based support vector machines, maximum likelihood and normalised difference vegetation index classification schemes are attempted to evaluate their performances towards crop classification. The linear imaging self-scanning (LISS-IV) multi-spectral sensor data was evaluated for the classification of crop types such as barley, wheat, lentil, mustard, pigeon pea, linseed, corn, pea, sugarcane and other crops and non-crop such as water, sand, built up, fallow land, sparse vegetation and dense vegetation. To determine the spectral separability among crop types, the M-statistic and Jeffries–Matusita (J–M) distance methods have been utilised. The results were statistically analysed and compared using Z-test and χ2-test. Statistical analysis showed that the accuracy results using SVMs with polynomial of degrees 5 and 6 were not significantly different and found better than the other classifica...

Correcting radiometric biases is crucial prior to the use of satellite observations in a physically based retrieval or data assimilation system. This study proposes an algorithm – RARMA ( R adiometric A djustment using R educed M ajor A xis) for correcting the radiometric biases so that the observed radiances coincide with the simulation of a radiative transfer model. The RARMA algorithm is a static bias correction algorithm, which is developed using the reduced major axis (RMA) regression approach. NOAA's Community Radiative Transfer Model (CRTM) has been used as the basis of radiative transfer simulation for adjusting the observed radiometric biases. The algorithm is experimented and applied to the recently launched Global Precipitation Measurement (GPM) mission's GPM Microwave Imager (GMI). Experimental results demonstrate that radiometric biases are apparent in the GMI instrument. The RARMA algorithm has been able to correct such radiometric biases and a significant reduction of observation residuals is revealed while assessing the performance of the algorithm. The experiment is currently tested on clear scenes and over the ocean surface, where, surface emissivity is relatively easier to model, with the help of a microwave emissivity model (FASTEM-5).

The conservation and protection of painted rock shelters is an important issue. Throughout the world, if unprotected, they are vulnerable to vandalism or to industrial activities such as quarrying. This research explores the integrated use of a Geographic Information System (GIS) with a multi-criteria decision support system and fuzzy logic to identify possible rock art sites over the Vindhyan Plateau in the district of Mirzapur, Central India. The methodology proposed compares results obtained by spatial modelling with validation data derived from recent exhaustive field surveys of more than forty newly discovered rock-shelters in the Vindhyan region. The zones obtained by predictive modelling are in agreement with validation datasets and show that the method can be used for new site prospection. This method represents a potential tool for landscape planners and policy makers to employ when seeking protection from anthropogenic activities of potential areas of painted rock-shelter sites and archaeological deposits.

A new method for obtaining the C factor (i.e., vegetation cover and management factor) of the RUSLE model is proposed. The method focuses on the derivation of the C factor based on the vegetation density to obtain a more reliable erosion prediction. Soil erosion that occurs on the hillslope along the highway is one of the major problems in Malaysia, which is exposed to a relatively high amount of annual rainfall due to the two different monsoon seasons. As vegetation cover is one of the important factors in the RUSLE model, a new method that accounts for a vegetation density is proposed in this study. A hillslope near the Guthrie Corridor Expressway (GCE), Malaysia, is chosen as an experimental site whereby eight square plots with the size of $$8\times 8$$ and $$5\times 5$$ m are set up. A vegetation density available on these plots is measured by analyzing the taken image followed by linking the C factor with the measured vegetation density using several established formulas. Finally, erosion prediction is computed based on the RUSLE model in the Geographical Information System (GIS) platform. The C factor obtained by the proposed method is compared with that of the soil erosion guideline Malaysia, thereby predicted erosion is determined by both the C values. Result shows that the C value from the proposed method varies from 0.0162 to 0.125, which is lower compared to the C value from the soil erosion guideline, i.e., 0.8. Meanwhile predicted erosion computed from the proposed C value is between 0.410 and $$3.925\, \hbox {t ha}^{-1 }\,\hbox {yr}^{-1}$$ compared to 9.367 to $$34.496\, \hbox {t ha}^{-1}\,\hbox {yr}^{-1 }$$ range based on the C value of 0.8. It can be concluded that the proposed method of obtaining a reasonable C value is acceptable as the computed predicted erosion is found to be classified as a very low zone, i.e. less than $$10\, \hbox {t ha}^{-1 }\,\hbox {yr}^{-1}$$ whereas the predicted erosion based on the guideline has classified the study area as a low zone of erosion, i.e., between 10 and $$50\, \hbox {t ha}^{-1 }\,\hbox {yr}^{-1}$$ .

An integrated Markov Chain and Cellular Automata modelling (CA MARKOV), multicriteria evaluation techniques have been applied to produce transition probability. The unsupervised method was employed...

Atmospheric aerosols are multicomponent mixtures typically composed of liquid and/or solid particles suspended in the atmosphere. Aerosols originate from numerous sources, having successively evolved through various microphysical processes before being removed either by wet or dry depositions. The implications of these airborne particulates on the regional and global climate are many but most notably through regulating the atmospheric heat budget either by absorbing/scattering insolation or by modifying cloud microphysical properties. Global distributions of aerosols are typically regional; thereby, they pose a strong regional signature that induces additional uncertainties in estimating aerosols, induced climate forcing. Satellite remote sensing of aerosols has extensive applications in identifying aerosol columnar properties, especially in terms of optical depth, composition, morphology, and vertical distribution. This ultimately provides evidence in establishing the source-transport-receptor relations of aerosols over a synoptic scale. Further, satellite data of atmospheric compositions are often used for identifying pollutant emissions, transboundary movement, forecasting air quality, and, more recently, in associating air quality with human health. The principles of remote sensing of aerosols are quite different from that of trace gases as the extinction of light by aerosols is a function of wavelength. With the gradual advancement of sensing technologies, monitoring of atmospheric aerosols has become more precise. Therefore, it has become more widely applied in various academic disciplines, studies, policies, and decision-making processes. This article emphasizes the state of the art in the field of satellite remote sensing, specifically in terms of polar-orbiting satellites for tropospheric aerosols including both active- and passive-based observations; associated complexities and uncertainties; brief descriptions of data products; and the subsequent applications in climate science.

The study of long-term precipitation record is critically important for a country, whose food security and economy rely on the timely availability of water. In this study, the historical 102-year (1901–2002) rainfall data of the Sindh River basin (SRB), India, were analyzed for seasonal and annual trends. The Mann–Kendall test and Sen's slope model were used to identify the trend and the magnitude of the change, respectively. Spatial interpolation technique such as Kriging was used for interpolating the spatial pattern over SRB in GIS environment. The analysis revealed the significantly increasing precipitation trend in both seasonal and annual rainfall in the span of 102 years.

Reference evapotranspiration (ETo) is an important variable in hydrological modeling, which is not always available, especially for ungauged catchments. Satellite data, such as those available from the MODerate Resolution Imaging Spectroradiometer (MODIS), and global datasets via the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis (ERA) interim and National Centers for Environmental Prediction (NCEP) reanalysis are important sources of information for ETo. This study explored the seasonal performances of MODIS (MOD16) and Weather Research and Forecasting (WRF) model downscaled global reanalysis datasets, such as ERA interim and NCEP-derived ETo, against ground-based datasets. Overall, on the basis of the statistical metrics computed, ETo derived from ERA interim and MODIS were more accurate in comparison to the estimates from NCEP for all the seasons. The pooled datasets also revealed a similar performance to the seasonal assessment with higher agreement for the ERA interim (r = 0.96, RMSE = 2.76 mm/8 days; bias = 0.24 mm/8 days), followed by MODIS (r = 0.95, RMSE = 7.66 mm/8 days; bias = −7.17 mm/8 days) and NCEP (r = 0.76, RMSE = 11.81 mm/8 days; bias = −10.20 mm/8 days). The only limitation with downscaling ERA interim reanalysis datasets using WRF is that it is time-consuming in contrast to the readily available MODIS operational product for use in mesoscale studies and practical applications.

Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.

Analysis of Earth observation (EO) data, often combined with geographical information systems (GIS), allows monitoring of land cover dynamics over different ecosystems, including protected or conservation sites. The aim of this study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National Park (RNP) during the period of 19 years (1990–2009). Several statistics such as principal component analysis (PCA) and spatial metrics are used to understand the results. PCA analysis has produced two principal components (PC) and explained 84.1% of the total variance, first component (PC1) accounted for the 57.8% of the total variance while the second component (PC2) has accounted for the 26.3% of the total variance calculated from the core area metrics, distance metrics and shape metrics. Our results suggested that notable changes happened in the RNP landscape, evidencing the requirement of taking appropriate...

The monitoring of urban cover is very important for the planner, management, governmental and non-governmental organizations for optimizing the use of urban resources and minimizing the environmental losses. The study here aims at analyzing the changes that occurred in urban green cover over a time span of 1991-2001 using multi-date Landsat satellite images data over the Varanasi district, India and its relation to demographical changes. The Support Vector Machines (SVMs) classifier has been used for image classification. The urbanization indicators such as Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used in order to understand the changes in urban cover and population dynamics. All the analysis indicates significant changes in the urban cover values with increasing population at both spatial and temporal scale.

The work presented here showed a comprehensive evaluation of dual-polarimetric RISAT-1 data for land use/land cover (LULC) classification. The textural images were extracted with the help of gray-level co-occurrence matrix approach. Analysis of inter-class separability using transformed divergence method was performed to recognize the potential textural images. The best combination of textural images was also identified on the basis of standard deviation of preferred textural images and correlation coefficients. The maximum likelihood classifier-based classification results for different scenarios were compared. Furthermore, various classification algorithms, maximum likelihood classifier (MLC), artificial neural network (ANN), random forest (RF) and support vector machine (SVM), were performed on the best identified scenario in order to observe the most suitable algorithm for LULC classification. The combination of radiometric and their related textural images was found improving the overall classification accuracy than individual datasets. The highest overall classification accuracy was found using SVM (88.97%) followed by RF (88.45%), ANN (83.65%) and MLC (78.18%).

In natural ecosystems, the linkages between inputs of carbon from plants, soil moisture (SM) and microbial activity are central to our understanding of nutrient cycling. Predictions of microbial activities in soil are important as they indicate the potential of the soil to support biochemical processes that are essential for the maintenance of soil fertility as well as productivity. The dehydrogenase activity (DHA) in soil provides information on microbial activities of the soil. However, estimation of DHA activity over complex terrain such as soils of the central Himalaya is not always possible due to very harsh environment and climatic conditions. In this study, the attempts were made to estimate the DHA in the soil of mid altitude central Himalaya using computational intelligence techniques. The linear and non-linear correlation results indicate that the fluctuations in SM and organic carbon (OC) in the root zone affect DHA and can be used as predictors for DHA. Therefore, the performances of support vector machines (SVMs) and generalized linear models (GLMs) were attempted for the prediction of DHA over mid altitude central Himalaya using information of SM and OC. The results showed that the SVM was giving a much better performance than GLM using SM and OC and could be promising and cost effective approach for soil DHA prediction over complex ecosystem. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which SM and OC datasets are used.

Reference Evapotranspiration (ETo) and soil moisture deficit (SMD) are vital for understanding the hydrological processes, particularly in the context of sustainable water use efficiency in the globe. Precise estimation of ETo and SMD are required for developing appropriate forecasting systems, in hydrological modeling and also in precision agriculture. In this study, the surface temperature downscaled from Weather Research and Forecasting (WRF) model is used to estimate ETo using the boundary conditions that are provided by the European Center for Medium Range Weather Forecast (ECMWF). In order to understand the performance, the Hamon’s method is employed to estimate the ETo using the temperature from meteorological station and WRF derived variables. After estimating the ETo, a range of linear and non-linear models is utilized to retrieve SMD. The performance statistics such as RMSE, %Bias, and Nash Sutcliffe Efficiency (NSE) indicates that the exponential model (RMSE = 0.226; %Bias = −0.077; NSE = 0.616) is efficient for SMD estimation by using the Observed ETo in comparison to the other linear and non-linear models (RMSE range = 0.019–0.667; %Bias range = 2.821–6.894; NSE = 0.013–0.419) used in this study. On the other hand, in the scenario where SMD is estimated using WRF downscaled meteorological variables based ETo, the linear model is found promising (RMSE = 0.017; %Bias = 5.280; NSE = 0.448) as compared to the non-linear models (RMSE range = 0.022–0.707; %Bias range = −0.207–−6.088; NSE range = 0.013–0.149). Our findings also suggest that all the models are performing better during the growing season (RMSE range = 0.024–0.025; %Bias range = −4.982–−3.431; r = 0.245–0.281) than the non−growing season (RMSE range = 0.011–0.12; %Bias range = 33.073–32.701; r = 0.161–0.244) for SMD estimation.

Updated and accurate information of rice-growing areas is vital for food security and investigating the environmental impact of rice ecosystems. The intent of this work is to explore the feasibility of dual-polarimetric C-band Radar Imaging Satellite-1 (RISAT-1) data in delineating rice crop fields from other land cover features. A two polarization combination of RISAT-1 backscatter, namely ratio (HH/HV) and difference (HH−HV), significantly enhanced the backscatter difference between rice and nonrice categories. With these inputs, a QUEST decision tree (DT) classifier is successfully employed to extract the spatial distribution of rice crop areas. The results showed the optimal polarization combination to be HH along with HH/HV and HH−HV for rice crop mapping with an accuracy of 88.57%. Results were further compared with a Landsat-8 operational land imager (OLI) optical sensor-derived rice crop map. Spatial agreement of almost 90% was achieved between outputs produced from Landsat-8 OLI and RISAT-1 data. The simplicity of the approach used in this work may serve as an effective tool for rice crop mapping.

Drought is a recurrent phenomenon in the semiarid regions of India that significantly affects the regional social, economic, and environmental conditions. Drought monitoring and assessment are challenging especially for regions that have sparse or very limited rain gauge observation networks. In this study, a comparative analysis was performed between satellite precipitation products including Tropical Rainfall Measuring Mission (TRMM-3B42), Climate Hazards Group InfraRed Precipitation (CHIRP) and ground-measured Indian Meteorological Department (IMD) precipitation data over the Bundelkhand region of Uttar Pradesh, India to assess the meteorological drought in this region. The district wise study was done to determine the regional differences among these satellite precipitation products and to statistically verify their performance in estimating the degree and spatial pattern over the study area. The Standardized Precipitation Index (SPI) was computed using the open-source DRINC software. The IMD derived SPI was found more correlated with TRMM compared to CHIRP for SPI-1, 3 and 12 with r values 0.74, 0.81 and 0.65 respectively. However, SPI-6 shows low positive and negative and correlation for both TRMM and CHIRP data. The current case study highlights the outperformance of TRMM data that enables real-time drought assessment owing to better accuracy and higher spatiotemporal resolution.

Summarization: Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology pro- vides an informative source of data covering the entire globe in a spatial and spectral resolution appropriate to better and easier classify land cover than traditional or conventional methods. The use of high spatial and spectral resolution imagery from EO sen- sors has increased remarkably over the past decades, as more and more platforms are placed in orbit and new applications emerge in different disciplines. The aim of the present review work is to provide all-inclusive critical reflection on the state of the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of satellite data and classification approaches employed in land cover classification. The review concludes providing recommendations and remarks on what should be done in order to overcome hurdle faced using above-mentioned problems in LULC mapping. This also provides information on using classifier algorithms depending upon the data types and dependent on the regional ecosystems. One of the main messages of our review is that in future, there will be a need to assemble techniques specifically used in LULC with their merit and demerits that will enable more comprehensive understanding at regional or global scale and improve understanding between different land cover relationship and variability among them and these remains to be seen. Presented on